u/Ok-Inspection7725

I need recommendations on the model settings to get optimal performance in oMLX. I am using the latest 0.6.2 version. I have a Macbook Pro with M1 Max and 64GB ram.

• What context window should I use? Should I use 128k or 256k?
• Which one will deliver better performance, TurboQuant or Lightning MTP?
• If I use Lightning MTP, what would be the recommended context window?

I am using omlx and local models particularly for agentic coding via VS Code and OpenCode.

I've read so many threads mentioning (whether directly or indirectly) that they are using Qwen3.6 27B or 35B models with less than 128k context length/window (e.g. 64k or 32k) while the model cards explicity say that to 'preserve thinking capabilities', using a 128k context length is recommended. Here's a screenshot of that statement from the model card:

https://preview.redd.it/fy0vb4puc9fh1.png?width=1280&format=png&auto=webp&s=7e5cb1f4eccf472d0455b72f4b44c362586808cb

I'm genuinely curious how using 128k context length is working out for those people. Does it affect the quality of the outputs? How does that work for those who are using these models for coding? if it's 64k how long does that last on coding tasks?

I am an experienced triathlete who is very data driven when it comes to my trainings. I have been using my Amazfit T-Rex Pro 3 for about 3 months now and it’s been great! I came from a Garmin Epix 3 Pro Gen 2 which I loved but then I became disappointed with how Garmin just abandons their old flagship devices and not provide new features in their firmware updates like other popular watches. So I’ve been looking for alternatives and I found Amazfit which have premium materials and a good software. I use it pretty much on all of my workouts and trainings, except swimming which I had to revert to using my Garmin, just for the swimming! This is due to the lack of swimming drill support on the the Amazfit watch, which is quite disappointing quite frankly as the drills are not counted in my swim mileage like in Garmin.

So Amazfit, please implement a swim drill support for you pool swim workout. Or at least let us be able to manually edit a lap distance.

Thanks in advance!

I am loving oMLX and I can notice the difference in performance. I am using it with Open WebUI but I've been getting this prefill issue lately and I'm kind of hoping, someone can help me make sense of it. Here's the error being returned:

Prefill would require ~52.38 GB peak (current 44.85 GB + KV+SDPA 7.53 GB) but metal_cap ceiling is 50.00 GB. Raise kernel iogpu.wired_limit_mb in Terminal (currently caps Metal at 58.00 GB), or reduce context length.

My machine:

M1 Max Macbook Pro

64GB of unified ram

Model: Gemma 4 26B A4B opus distilled model

I am not sure if this is a bug with oMLX or it could be linked to that jinja template issue the Gemma models have been encountering. I tried using LM Studio to carry on with the task (same thread where I got the prefill error), and it seems to be working fine.

Would appreciate it if someone can point me towards the right direction, thanks in advance!